Search Results (182)

Ceramic additive manufacturing offers strong potential for fabricating geometrically complex and application-specific components, yet achieving reliable dimensional fidelity remains challenging because dimensional deviation is governed by highly coupled material, process, thermal, and environmental factors. To address this problem, this study proposes an uncertainty-aware computational framework for dimensional error prediction in ceramic 3D printing under variable material and process conditions. The contribution is positioned as a system-level integration of established learning, uncertainty estimation, calibration, and reliability-interpretation components within a ceramic additive manufacturing dimensional-error prediction workflow, rather than as a fundamental methodological breakthrough. The validation is conducted using the publicly available Ceramic 3D Printing Process Control Dataset, a 1000-sample tabular dataset, and the resulting findings are therefore interpreted as dataset-specific computational evidence rather than direct proof of industrial deployment readiness. The methodology begins with a structured data-driven preprocessing pipeline that transforms the Ceramic 3D Printing Process Control Dataset into a multi-condition feature space through data cleaning, one-hot material encoding, min–max normalization, and engineered descriptors capturing extrusion–speed balance, thermal gradients, cooling intensity, deposition density, and material-conditioned interactions. A multi-branch deep computational architecture is then developed to encode material, process, thermal-environmental, and engineered-feature streams separately, followed by adaptive cross-condition fusion to learn nonlinear dependencies across ceramic printing regimes. To improve reliability beyond deterministic regression, the framework jointly models aleatoric and epistemic uncertainty and incorporates calibration refinement to align predictive confidence with observed error behavior, thereby enabling preliminary reliability-oriented interpretation of stable and high-risk operating conditions. Experimental results demonstrate that the full model achieves the best overall within-dataset performance, with a test MAE of 0.0118, RMSE of 0.0172, $R^2=0.999$, MAPE of 1.74%, calibration error of 0.003, PICP of 0.996, reliability score of 0.992, and a stable prediction rate of 98.7%. Although these values indicate strong predictive behavior under the current structured dataset, the exceptionally high $R^2$ should be interpreted cautiously because external experimental validation, larger measured datasets, and cross-machine ceramic printing trials are still required. These findings show that the proposed framework provides an effective system-level computational strategy for dataset-specific reliability-aware dimensional quality prediction in ceramic additive manufacturing and offers a preliminary data-driven foundation for uncertainty-aware intelligent process optimization. Full article

In precision livestock farming, 3D point clouds provide important data support for analyzing pig behavior and monitoring their health. However, due to environmental occlusions, limited sensor viewpoints, and mutual shielding between pigs, the acquired point clouds are often severely partial, which affects the accuracy of body shape modeling and behavior recognition. To address these challenges, this study constructed a pig pose point cloud dataset using multi-view depth camera acquisition and point cloud registration techniques. Based on this dataset, an improved point cloud completion model, IIR-PoinTr, is proposed to enhance the reconstruction of geometric and topological structures in pig bodies. By strengthening local geometric perception and high-dimensional feature representation, the model improves the reconstruction quality of partial pig point clouds and produces more structurally consistent pig body shapes. Experimental results show that, on the self-constructed pig posture dataset, the proposed method reduces Chamfer Distance (CD-L1) by 3.6%, CD-L2 by 6.9%, and Earth Mover’s Distance (EMD) by 2.0%, while improving the F-score by 5.4% compared with the baseline model. In single-view point cloud completion tasks, the method is capable of reconstructing geometrically consistent pig body structures and increases downstream classification accuracy by 34.9%. These results indicate that the proposed method can improve the reconstruction quality of partial pig point clouds and provide preliminary technical support for posture analysis under occlusion. Full article

Object detection in UAV remote sensing imagery remains challenging due to severe scale variation, dense object distributions, complex background clutter, and localization ambiguity caused by extremely small objects. To address these issues, this paper proposes BDRNet, a lightweight background-aware dynamic-scale routing network for UAV remote sensing object detection. First, a background-aware feature enhancement (BAFE) module is introduced into the backbone to enhance feature representation through horizontal and vertical contextual modeling, improving target-related responses in complex aerial scenes. Second, a dynamic-scale routing pyramid (DSRP) is designed to retain the high-resolution $P_2$ branch and adaptively integrate multi-scale features through spatially dynamic routing, alleviating the loss of fine-grained information and improving the representation of small and scale-varied objects. Third, a scale- and geometry-aware normalized Wasserstein distance (SGNW) loss is proposed by modeling bounding boxes as two-dimensional Gaussian distributions. By incorporating aspect-ratio-guided geometric weighting and scale-aware dynamic fusion, SGNW improves regression stability for small objects while preserving geometric constraints for medium and large targets. Extensive experiments on the VisDrone2019 and UAVDT datasets demonstrate that BDRNet consistently improves detection accuracy over the YOLOv10s detector while maintaining a comparable model size and computational cost. Compared with several mainstream lightweight detectors, BDRNet achieves a favorable accuracy–efficiency trade-off, demonstrating its effectiveness for UAV remote sensing object detection in complex aerial scenarios. Full article

In impact testing, evaluating multiple-impact signals is critical for verifying whether a test setup can reproduce penetration response signals and ensure reliable results. To overcome the limitations of traditional methods, including incomplete indicator coverage, subjective weighting, and poor consistency, this study proposes a fuzzy comprehensive evaluation (FCE) framework based on time–frequency features and combined weighting. Using multi-layer penetration response signals as the matching target, a multidimensional indicator system covering time-domain features, frequency-domain features, and signal quality and stability is established. A combined weighting method integrating AHP, EWM, and CRITIC is then developed, and subjective and objective weights are fused using the geometric mean method. A fuzzy comprehensive evaluation model is used to quantify the matching degrees of multiple sets of multiple-impact signals, and robustness is verified through weight consistency tests and sensitivity analysis. The results show that the evaluated signal sets are rated “Excellent”. Under reasonable weight combinations, the probability of obtaining an “Excellent” result reaches 99.94%, and the maximum variation caused by a ±10% perturbation in a single indicator weight is only 0.0087. The proposed framework provides a practical tool for evaluating multi-layer penetration response simulations and can be extended to other complex dynamic signal-matching problems. Full article

This review explicitly focuses on agricultural attachments and executing components that interact directly with soil and crops, rather than the tractor vehicle itself. Operating within complex and variable farmland media environments, the key components of agricultural machinery have long been constrained by bottlenecks such as high-energy draught resistance, severe solid–liquid interfacial adhesion, and intense abrasive wear. Bionic functional surfaces, based on the coupling of micro-geometric morphology and surface-interface physical chemistry, provide a scientific approach to overcoming traditional tribological limitations by reconstructing the contact mechanics and fluid dynamics boundaries at the interface. This paper presents a comprehensive review of the latest research progress regarding bionic functional surfaces in the fields of friction reduction, wear resistance, and anti-adhesion in agricultural machinery. The article systematically categorises typical biological prototypes, such as soil-burrowing animals, aquatic organisms, and plant leaves, alongside their multidimensional feature extraction methods. It provides an in-depth analysis of core interaction mechanisms, ranging from static air cushion effects and dynamic wetting evolution to active electro-osmotic soil detachment, interfacial stress redistribution, and microscopic wear debris capture. Furthermore, it evaluates the efficacy of cross-scale coupled numerical simulation technologies in resolving interfacial interactions. At the engineering application level, this review extensively discusses the field performance of bionic structures in typical operational scenarios, including draught reduction in tillage and land preparation, blockage prevention in seed-metering channels, and low-damage harvesting in agricultural machinery. Finally, countermeasures are proposed to address the fatigue degradation of bionic surfaces under alternating field loads and the barriers to the large-scale fabrication of large-sized components. The paper further highlights the development trend towards the deep integration of bionic tribology with digital twins and intelligent wear-state perception technologies, aiming to provide systematic underlying theoretical and technical references for the research and development of the next generation of intelligent agricultural equipment characterised by low energy consumption and a prolonged service life. Full article

Synthetic Aperture Radar (SAR) has broad application prospects in target recognition; however, intrinsic multiplicative speckle noise, geometric distortions, and the complex coupling of multimodal features often limit the comprehensiveness of representations and the efficiency of fusion, thereby restricting recognition accuracy. To address these limitations, this paper proposes a SAR image target recognition method based on multidimensional feature fusion. The proposed method first achieves noise suppression and contrast enhancement through an optimized preprocessing layer. Subsequently, a dual-branch hierarchical feature extraction network synchronously captures low-dimensional physical prior features driven by domain knowledge and highly discriminative deep convolutional features, ensuring a balance between physical interpretability and high-capacity representation. Finally, a variance-adaptive weighted fusion layer dynamically balances the contribution of different feature streams, mitigating information redundancy and feature conflict. Quantitative experiments on the MSTAR and public CETC38-SAR datasets demonstrate that under various pre-trained backbones, the proposed framework improves precision, recall, and F1-score by 5%–15% compared with baseline methods. Ablation studies and evaluations under extended operating conditions further validate the robustness, computational efficiency, and structural validity of the decoupled architecture. Full article

To overcome the inherent limitations of conventional offline programming in adapting to dimensional deviations and assembly-induced errors during robotic welding of ship structures, this paper proposes a point-cloud-enhanced visual scanning paradigm that enables automatic weld seam identification and collision-free trajectory planning. A dedicated monochromatic vision system is rigidly integrated onto a six-axis industrial robot, enabling high-fidelity feature extraction and geometric contour reconstruction for the precise localization of multi-configuration weld seams. The proposed approach substantially reduces manual teaching operations, enhances environmental adaptability in unstructured shipbuilding workshops, and improves global positioning accuracy. The core technical contributions are threefold: (1) systematic design and precision calibration of the integrated robotic vision system, including a hand–eye calibration procedure; (2) development of a hybrid 2D image-3D point cloud processing pipeline that combines SURF and FLANN for image stitching with RANSAC-based plane segmentation and PCA-driven contour reconstruction; and (3) extensive experimental validation across five distinct workpiece configurations. These results confirm the system’s strong applicability for intelligent and efficient shipbuilding welding, significantly outperforming conventional offline programming, which exhibits deviations exceeding 5 mm under identical conditions. Quantitative error analysis demonstrates that the online recognition method achieves a weld localization root mean square error (RMSE)of 0.82 mm, a standard deviation of 0.45 mm, and a verified maximum absolute deviation of 1.5 mm. Full article

Cooperative deployment of mobile agents under geometric and line-of-sight constraints gives rise to high-dimensional constrained optimization problems whose underlying physical configuration often exhibits exploitable structure. This paper develops a symmetry-driven inverse design framework that leverages two structural features of the engagement geometry—the ${\mathbb{Z}}_2\times {\mathbb{Z}}_2$ mirror symmetries of the extended target silhouette and a closed-form forward–inverse correspondence between line-of-sight-aligned burst locations and physical agent parameters—to construct low-dimensional seeds for subsequent physical parameter optimization. The framework is developed and validated on a representative naval defense instance in which a fleet of unmanned aerial vehicles (UAVs) releases spherical obscuration payloads to interrupt the line of sight between incoming mobile threats and a cylindrical extended target. Instead of searching only over the four-dimensional UAV parameter space (heading angle, speed, drop time, fuse delay), the method first specifies a desired burst location in a two-dimensional inverse space and analytically back-calculates feasible agent parameters, which are then refined by multi-start Nelder–Mead optimization in the physical parameter space. A conservative three-dimensional cylindrical line-of-sight obscuration model is developed by constructing four extreme tangent sightlines from the missile to the cylindrical target and verifying whether the spherical smoke cloud simultaneously blocks all of them. A hierarchical multi-agent task allocation framework combines a performance matrix, assignment enumeration, and joint multi-start refinement. Numerical experiments on five progressively complex sub-problems demonstrate obscuration durations of $1.362 \mathrm{s}$ (single fixed shot), $4.580 \mathrm{s}$ (optimized shot), $7.324 \mathrm{s}$ (three-shot relay), $11.140 \mathrm{s}$ (three-UAV cooperation), and $20.652 \mathrm{s}$ (full five-UAV three-missile assignment). Additional high-dimensional benchmarks, sensitivity tests, and error analyses clarify the reproducibility and limitations of the approach. Full article

Three-dimensional (3D) shape recognition is a fundamental task in computer vision, where view-based methods have recently achieved state-of-the-art performance. However, effectively capturing and exploiting the rich geometric correspondences between different views remains a key challenge, as such information is crucial for accurate shape representation. Existing methods often fall short in explicitly modeling these structured correlations, which limits their ability to fully leverage discriminative shape information. To address this limitation, we propose a novel View-based Graph Convolution and Sampling Fusion Network (View-GFN). View-GFN employs a hierarchical architecture that progressively coarsens the view-graph to learn multi-scale features. In this structure, views are treated as graph nodes, and a predefined-value strategy is introduced to initialize the adjacency matrix (AM) for constructing initial node correlations. For effective graph coarsening, we develop a novel view down-sampling method based on a cluster assignment matrix. Furthermore, a Graph Convolution and Sampling Fusion (CSF) module is designed to seamlessly integrate deep feature embeddings with the topological information derived from view down-sampling. Extensive experiments on benchmark datasets, including ModelNet40 and RGB-D, demonstrate that View-GFN achieves strong performance, performing on par with established baseline methods while reducing the number of model parameters by nearly 50% compared to the baseline View-GCN. These results validate the effectiveness of our hierarchical fusion strategy in capturing multi-view geometric information both efficiently and robustly. Full article

Northern Chinese rock art images are characterized by abstract structures, significant morphological variation, blurred carving traces, and complex background textures. These factors pose substantial challenges to automatic detection and classification. To address these issues, this study develops an improved object detection model, termed YOLOv8-ODConv, based on the YOLOv8 framework for rock art image recognition. The proposed model integrates Omni-Dimensional Dynamic Convolution (ODConv) into the key layers of the Backbone and Neck. This design enables convolution kernels to adapt dynamically across spatial, channel, and kernel dimensions, thereby enhancing the representation of complex carving structures, fine-grained local differences, and multi-scale features. A dedicated dataset of Northern Chinese rock art images is constructed, including three representative categories: Anthropomorphic_Face, Deer, and Human_Horse. To improve model robustness under challenging visual conditions, multiple data augmentation strategies are applied, including brightness variation, noise perturbation, and geometric transformations. Experimental results demonstrate that YOLOv8-ODConv outperforms SSD, YOLOv5, YOLOv8, and YOLOv8–Backbone–ODConv across multiple evaluation metrics. The proposed model achieves an F1-score of 95.0%, Recall of 89.3%, mAP@0.5 of 98.9%, mAP@0.5–0.95 of 84.6%, and an inference speed of 89.7 FPS. Confusion matrix analysis and visual detection results further confirm that the model effectively distinguishes typical rock art categories and maintains stable performance in complex backgrounds and fine-grained recognition tasks. These findings indicate that YOLOv8-ODConv provides an effective technical approach for the digital documentation, automatic recognition, and intelligent analysis of rock art cultural heritage. Full article

The spectral selectivity of underwater multiwavelength single-photon LiDAR offers a promising pathway to discriminate target materials beyond conventional geometric imaging. However, the complex interactions among wavelength-dependent water attenuation, target reflectance, and scattering-induced waveform distortion remain poorly quantified. This study establishes a comprehensive theoretical and experimental framework linking these factors, validated through controlled experiments across two water turbidity levels (attenuation coefficients of 0.1 m⁻¹ and 2.0 m⁻¹), six wavelengths (490–570 nm), and diverse target types. We demonstrate that target ranging bias exhibits a wavelength-dependent linear trend (8.3 ps/nm) in turbid waters. This phenomenon is fundamentally attributable to forward-scattering-induced centroid shifts rather than true spatial displacements, a mechanism we quantify through comparative peak-detection and Gaussian fitting analyses. Contrary to intuitive expectations, we reveal that spectral discrimination efficacy decouples from received photon counts. Principal component analysis confirms that a multidimensional spectral feature space enables accurate target clustering independent of absolute intensity, with specific bands (e.g., 510 nm and 550 nm) exhibiting heightened sensitivity to material signatures. These findings establish that underwater target recognition is primarily influenced by the spectral contrast between target reflectance and water transmission windows, rather than solely depending on received photon counts, providing a robust physical basis for next-generation underwater LiDAR optimization. Full article

Regular geometric targets under microscopic scenes, such as microspheres, micropores, and microtubes, are characterized by small scales, low contrast, and degraded boundaries. Masks generated by general segmentation methods often fail to directly support high-precision geometric parameter measurement. This paper proposes a mask optimization method for the high-precision extraction of regular geometric features in microscopic scenes. We establish a mask optimization framework that integrates initial mask generation with geometric consistency refinement. Mask initialization is first performed through segmentation and adaptive super-resolution (SR) under low annotation constraints. Subsequently, an iterative optimization strategy that fuses multi-dimensional pixel features with regular geometric priors is designed. By incorporating geometric features extracted from the current mask while maintaining stable pixel-level observations, the mask is progressively corrected until convergence to generate target masks with continuous boundaries that satisfy stringent geometric constraints. Our experimental results on a sphere–tube assembly dataset demonstrate that the proposed method achieves lower geometric errors on successfully fitted samples and significantly improves the fitting success rate. Ablation studies further confirm the critical roles of dynamic SR and iterative mask optimization in enhancing overall precision and stability. These findings suggest that for microscopic regular geometric measurement tasks, integrating geometric-consistency constraints into mask optimization effectively improves both the accuracy and robustness of geometric feature extraction. Full article

Accurate mapping of winter rapeseed cultivation areas is crucial for food security assessment and agricultural resource management, yet remains a persistent challenge in mountainous regions characterized by complex topography and highly fragmented field parcels. To address these challenges, this study develops a progressive deep learning framework using single growing-season data from the Hanzhong Basin. We conducted a structured comparison of remote sensing indices, machine learning, and deep learning approaches for rapeseed identification in heterogeneous landscapes. First, sensitivity analysis of the Flowering Index for Rapeseed was performed to identify the optimal parameterization, yielding high inter-class separability (ND = 0.959) during peak flowering and a threshold-based overall accuracy (OA) of 94.41%. Second, a multidimensional feature space was constructed by integrating Sentinel-2 spectral bands, image texture metrics, and topographic variables; Random Forest-based feature importance selection subsequently enhanced Support Vector Machine classification performance to an OA of 90.70%. Third, we proposed an innovative three-stage progressive UNet++ architecture: Stage1 focuses on binary rapeseed/non-rapeseed classification to establish spatial priors; Stage2 refines discrimination among spectrally similar vegetation classes (rapeseed and other vegetation); and Stage3 achieves comprehensive seven-class semantic segmentation. A weighted focal loss function combined with a weight inheritance mechanism was employed to mitigate class imbalance and facilitate inter-stage knowledge transfer. The final model attained an OA of 98.65% and a mean intersection over union of 95.29%, while effectively suppressing salt-and-pepper noise artifacts in geometrically fragmented parcels. Our findings demonstrate the substantial advantages of progressive deep learning strategies for crop monitoring in topographically constrained environments. Full article

The gap dimension of a railway switch machine is a critical physical quantity for determining the locking status of railway turnouts. Under operating conditions characterized by heavy oil contamination, complex illumination, and equipment vibration, existing visual measurement methods often struggle to maintain stability and achieve sub-pixel precision. To address this issue, this paper proposes a gap measurement method based on the fusion of vision and geometric features (G-VFM). The method first utilizes a confidence-aware optimized YOLOv8 model to achieve robust localization of the gap region. Subsequently, an improved multi-channel U-Net is employed to extract soft-edge probability maps, based on which a 20-dimensional structured geometric descriptor is constructed. Finally, visual semantic features and geometric priors are fused for regression through an R34-Fusion two-stream residual network, and systematic errors are corrected using a weighted Huber loss combined with a piecewise linear calibration strategy. Test results on a constructed field dataset show that the proposed method achieves a Mean Absolute Error (MAE) of 0.0076 mm and a maximum error of 0.0193 mm. It achieves a 100% pass rate under an industrial tolerance of 0.02 mm, with an end-to-end inference time of 52.23 ms (~19.15 FPS), balancing both precision and efficiency. Further tests on illumination degradation, noise interference, and cross-batch evaluations indicate that the method maintains relatively stable performance across various complex scenarios. However, performance decreases significantly under extremely low-light conditions, suggesting that actual deployment may require integration with active lighting or multi-sensor fusion to ensure system reliability across all working conditions. Overall, this method achieves high-precision gap measurement under current experimental conditions and provides a feasible solution for vision-based switch machine status monitoring. Full article

In the assessment of bearing capacity for in-service bridge pile foundations, static load tests are costly, destructive, and difficult to scale. The traditional dynamic formula approach relies heavily on an empirical dynamic–static conversion coefficient that introduces considerable uncertainty. To address these limitations, this study proposes a non-destructive evaluation method for pile foundation bearing capacity based on measured dynamic stiffness and machine learning algorithms. Using data from a highway bridge inspection project, a dataset comprising 680 piles was compiled, including measured dynamic stiffness, geometric parameters, and design load information. An end-to-end binary classification model was constructed to map multidimensional physical features to an engineering decision target, namely, whether the bearing capacity meets the design requirement. The performance of several algorithms was compared, including logistic regression, random forest, and gradient boosting decision tree (GBDT). Among the evaluated models, the GBDT model demonstrated the best capability for capturing the complex nonlinear pile–soil interactions. On an independent test set, it achieved an accuracy of 96.3% and an F1 score of 0.96, with a very low false-negative rate, satisfying the high precision required for engineering safety screening. Feature importance analysis indicates that measured dynamic stiffness contributed approximately 42% to the classification outcome, establishing it as the dominant indicator for detecting capacity deficiencies and reinforcing its physical relevance as a key health indicator for pile foundations. This study demonstrates that data-driven methods can effectively circumvent the uncertainty associated with traditional empirical coefficients, providing a promising approach to the health monitoring and rapid evaluation of in-service bridge pile foundations. Full article